How to find Fourier Transform of pulse and apply it on filter response?

In summary: For example, if you had a pulse at 110 Hz and you wanted to transform it to a 555 Hz pulse, you would multiply the 110 Hz pulse by 1.10 (10%) and then add the 555 Hz pulse.
  • #1
russel.arnold
41
0
Hi all,

I have a filter whose amplitude and phase response in terms of w(omega) is known to me which i calculated numerically. Hence, the value is known only for discrete values of w(omega).

Now, I want to know the output which this filter will produce on sending a temporal pulse(pulse in time domain) as my input.

I know i need to Fourier transform my pulse( in the form summation(A(w)exp(iwt)) and then write the output as something like summation(A(w)*H(w)*exp(jwt)).

The question is how can i find the Fourier transform (i.e find A(w) at those w where response of filter is known)
 
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  • #2
russel.arnold said:
Hi all,

I have a filter whose amplitude and phase response in terms of w(omega) is known to me which i calculated numerically. Hence, the value is known only for discrete values of w(omega).

Now, I want to know the output which this filter will produce on sending a temporal pulse(pulse in time domain) as my input.

I know i need to Fourier transform my pulse( in the form summation(A(w)exp(iwt)) and then write the output as something like summation(A(w)*H(w)*exp(jwt)).

The question is how can i find the Fourier transform (i.e find A(w) at those w where response of filter is known)

Usually you model the transfer function as a polynomial of poles and zeros. You know the DC gain and you see that whenever the transferfunction slope changes +-20 dB/decade that you have a pole or zero at that place.

Edit: I think this is more commonly done in the laplace domain than in the Fourier domain.
 
  • #3
russel.arnold said:
Hi all,

I have a filter whose amplitude and phase response in terms of w(omega) is known to me which i calculated numerically. Hence, the value is known only for discrete values of w(omega).

Now, I want to know the output which this filter will produce on sending a temporal pulse(pulse in time domain) as my input.

I know i need to Fourier transform my pulse( in the form summation(A(w)exp(iwt)) and then write the output as something like summation(A(w)*H(w)*exp(jwt)).

The question is how can i find the Fourier transform (i.e find A(w) at those w where response of filter is known)

Well, since you have a filter whose amplitude and phase response in terms of w(omega) is known you can use those discrete values to determine the increment in which they are increasing/decreasing at... although I am not exactly sure how you have these values/the manner in which you calculated them.

When you transform your pulse (you never directly mentioned what kind) yielding a 'continuous' waveform, simply plug that incrementing discrete values of frequency in it. Remember, convolution in the Time Domain is multiplication in the Frequency Domain.
 

1. What is the difference between frequency and temporal domain?

The frequency domain refers to the representation of a signal in terms of its frequency components, while the temporal domain refers to the representation of a signal in terms of its time-varying amplitude.

2. How are frequency and temporal domains related?

The frequency and temporal domains are mathematically related through the Fourier transform, which allows for the conversion between the two representations of a signal.

3. Why is it important to analyze signals in both frequency and temporal domains?

Analyzing signals in both frequency and temporal domains allows for a more comprehensive understanding of the signal's characteristics and behavior. It can also help identify specific frequency components that may be of interest or importance.

4. What is the unit of measurement for frequency and temporal domains?

The unit of measurement for frequency is Hertz (Hz), while the unit for the temporal domain is typically seconds (s).

5. Can a signal have different frequency components in the frequency and temporal domains?

Yes, a signal can have different frequency components in the frequency and temporal domains. This is because the two representations are related but not identical, and certain factors such as sampling rate and windowing can affect the frequency components observed in each domain.

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